Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models
Rockfalls are among the frequent hazards in underground mines worldwide, requiring effective methods for detecting unstable rock blocks to ensure miners' and equipment's safety. This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and...
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| Format: | Article |
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KeAi Communications Co. Ltd.
2025-06-01
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| Series: | Artificial Intelligence in Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544125000024 |
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| author | Kaoutar Clero Said Ed-Diny Mohammed Achalhi Mouhamed Cherkaoui Imad El Harraki Sanaa El Fkihi Intissar Benzakour Tarik Soror Said Rziki Hamd Ait Abdelali Hicham Tagemouati François Bourzeix |
| author_facet | Kaoutar Clero Said Ed-Diny Mohammed Achalhi Mouhamed Cherkaoui Imad El Harraki Sanaa El Fkihi Intissar Benzakour Tarik Soror Said Rziki Hamd Ait Abdelali Hicham Tagemouati François Bourzeix |
| author_sort | Kaoutar Clero |
| collection | DOAJ |
| description | Rockfalls are among the frequent hazards in underground mines worldwide, requiring effective methods for detecting unstable rock blocks to ensure miners' and equipment's safety. This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and image segmentation techniques. Infrared images of rock blocks were captured at the Draa Sfar deep underground mine in Morocco using the FLUKE TI401 PRO thermal camera. Two segmentation methods were applied to locate the potential unstable areas: the classical thresholding and the K-means clustering model. The results show that while thresholding allows a binary distinction between stable and unstable areas, K-means clustering is more accurate, especially when using multiple clusters to show different risk levels. The close match between the clustering masks of unstable blocks and their corresponding visible light images further validated this. The findings confirm that thermal image segmentation can serve as an alternative method for predicting rockfalls and monitoring geotechnical issues in underground mines. Underground operators worldwide can apply this approach to monitor rock mass stability. However, further research is recommended to enhance these results, particularly through deep learning-based segmentation and object detection models. |
| format | Article |
| id | doaj-art-f8fc27332589451094df5bbee5a39077 |
| institution | Kabale University |
| issn | 2666-5441 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Geosciences |
| spelling | doaj-art-f8fc27332589451094df5bbee5a390772025-01-31T05:12:30ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412025-06-0161100106Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation modelsKaoutar Clero0Said Ed-Diny1Mohammed Achalhi2Mouhamed Cherkaoui3Imad El Harraki4Sanaa El Fkihi5Intissar Benzakour6Tarik Soror7Said Rziki8Hamd Ait Abdelali9Hicham Tagemouati10François Bourzeix11National School of Mines of Rabat (ENSMR), Morocco; Moroccan Foundation for Advanced Science, Innovation and Research (MASCIR), Morocco; Corresponding author. National School of Mines of Rabat (ENSMR), Morocco.National School of Mines of Rabat (ENSMR), MoroccoNational School of Mines of Rabat (ENSMR), MoroccoNational School of Mines of Rabat (ENSMR), MoroccoNational School of Mines of Rabat (ENSMR), MoroccoNational School of Computer Science and Systems Analysis (ENSIAS), MoroccoREMINEX Engineering, R&D and Project Management, MoroccoREMINEX Engineering, R&D and Project Management, MoroccoREMINEX Engineering, R&D and Project Management, MoroccoMohammed VI Polytechnic University, Analytics Lab, MoroccoMohammed VI Polytechnic University, Analytics Lab, MoroccoMohammed VI Polytechnic University, Analytics Lab, MoroccoRockfalls are among the frequent hazards in underground mines worldwide, requiring effective methods for detecting unstable rock blocks to ensure miners' and equipment's safety. This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and image segmentation techniques. Infrared images of rock blocks were captured at the Draa Sfar deep underground mine in Morocco using the FLUKE TI401 PRO thermal camera. Two segmentation methods were applied to locate the potential unstable areas: the classical thresholding and the K-means clustering model. The results show that while thresholding allows a binary distinction between stable and unstable areas, K-means clustering is more accurate, especially when using multiple clusters to show different risk levels. The close match between the clustering masks of unstable blocks and their corresponding visible light images further validated this. The findings confirm that thermal image segmentation can serve as an alternative method for predicting rockfalls and monitoring geotechnical issues in underground mines. Underground operators worldwide can apply this approach to monitor rock mass stability. However, further research is recommended to enhance these results, particularly through deep learning-based segmentation and object detection models.http://www.sciencedirect.com/science/article/pii/S2666544125000024Underground miningImage processingK-means clusteringInfrared thermal imagingGeotechnical monitoring |
| spellingShingle | Kaoutar Clero Said Ed-Diny Mohammed Achalhi Mouhamed Cherkaoui Imad El Harraki Sanaa El Fkihi Intissar Benzakour Tarik Soror Said Rziki Hamd Ait Abdelali Hicham Tagemouati François Bourzeix Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models Artificial Intelligence in Geosciences Underground mining Image processing K-means clustering Infrared thermal imaging Geotechnical monitoring |
| title | Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models |
| title_full | Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models |
| title_fullStr | Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models |
| title_full_unstemmed | Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models |
| title_short | Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models |
| title_sort | loosening rocks detection at draa sfar deep underground mine in morocco using infrared thermal imaging and image segmentation models |
| topic | Underground mining Image processing K-means clustering Infrared thermal imaging Geotechnical monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S2666544125000024 |
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